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\title{YOLOv3: An Incremental Improvement}

\author{Joseph Redmon \quad Ali Farhadi\\
{\normalsize University of Washington}
}
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\begin{abstract}

We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At $320\times320$ YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves $57.9$ AP$_{50}$ in 51 ms on a Titan X, compared to $57.5$ AP$_{50}$ in 198 ms by RetinaNet, similar performance but 3.8$\x$ faster. As always, all the code is online at \url{https://pjreddie.com/yolo/}.

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